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MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
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MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and apply it to U-Net its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.
Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and apply it to U-Net its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.
